Penn State National Science Foundation Center for Health Organization Transformation
(Penn State CHOT) - An Industry-University Cooperative Research Center
New Book 
Hui Yang, CHOT site director, and Robin Qiu, CHOT affiliated faculty member, along with the co-editor Weiwei Chen, edited and published a new book titled "Smart Service Systems, Operations Management, and Analytics"

The book offers state-of-the-art research in service science and its related research, education, and practice areas. It showcases recent developments in smart service systems, operations management, and analytics and their impact in complex service systems. The papers included in this volume highlight emerging technology and applications in fields including health care, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services aimed at meeting end users' utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions.  
Penn State CHOT Research Highlights  
A.I. could offer warnings about serious side effects of drug-drug interactions

Soundar Kumara, a CHOT affiliated faculty member, leads his team to develop a novel  artificial  intelligent approach to warn doctors and patients about serious side effects that may occur when drugs are mixed.

Identifying drug-drug interactions (DDIs) is a critical enabler for reducing adverse drug events and improving patient safety. The high number of possible adverse drug-drug interactions, which can range from minor to severe, may inadvertently cause doctors and patients to ignore alerts. To avoid alert fatigue, Soundar Kumara led a team to identify interactions that would only be considered high priority, such as life-threatening, disability, hospitalization, and required intervention. To create an effective alert system, Kumara's team developed an A.I. model to automatically label data from thousands of drugs and millions of different combinations of possible interactions, which assists in reducing the adverse events. Kumara said that analyzing how drugs interact is the first step. Further development and refinement of the technology could lead to more precise - and even more personalized - drug interaction alerts. Kumara presented this work at the Penn State Institute of CyberScience Seminar on Nov. 5, 2019. See the YouTube video of the presentation recording below.

Ning Liu, Cheng-Bang Chen, and Soundar Kumara. "Semi-Supervised Learning Algorithm for Identifying High-Priority Drug-Drug Interactions Through Adverse Event Reports." IEEE Journal of Biomedical and Health Informatics (2019). DOI: 10.1109/JBHI.2019.2932740
Substance use and misuse in our communities - opioid  crisis

Substance misuse and disorders grip our nation. Pennsylvania has especially been hit hard, with the third highest overdose death rate in the nation in 2017. From the opioid crisis, evidenced by the shockingly fast increase in the number of lives lost to overdose in recent years, to the emerging epidemic of youth vaping, all indications are that Pennsylvania, and the nation, must take sweeping action. Stephanie Lanza, a CHOT affiliated faculty member, pioneers a community-coordinated system for prevention, treatment, recovery, and prisoner reentry services to address today's opioid crisis and prevent the crisis of tomorrow. 

Please see the YouTube video below for Penn State's Bold Solutions to the Systemic Problems of Substance Misuse: A Coordinated, Statewide Plan to Prevent the Next Epidemic.

Penn State MacArthur Video
Heterogeneous Recurrence Analysis of Disease-altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals

Ruimin Chen, a CHOT scholar, developed a novel methodology of heterogeneous recurrence analysis to characterize and model disease-altered variations in the recurrence patterns of cardiac signals. 

Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and state transition dynamics). This research provides a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infractions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals toward medical decision making.

Ruimin Chen, Farhad Imani, and Hui Yang. "Heterogeneous Recurrence Analysis of Disease-altered Spatiotemporal Patterns in Multi-Channel Cardiac Signals." IEEE journal of biomedical and health informatics (2019). DOI: 10.1109/JBHI.2019.2952285

Breast cancer is the most common cancer for women, which leads to approximately 41,760 deaths annually in the United States. IDC is the most common form of breast cancer. The objective of this research is to identify IDC with unlabeled histopathology images. The dataset consists of 277,524 images that are small patches extracted from digital images of breast tissue samples. Note that it takes a significant amount of time and effort for human experts to examine these images. Machine learning methods help automate the process, save time, and reduce error. Hence, this research developed a new machine learning method, named heterogeneous recurrence analysis of spatial data, to characterize histology images and predict IDC in breast cancer. The model performance reaches 96% in terms of Area Under the ROC Curve (AUC), which is comparable to a well-trained human expert.

CHOT Undergraduate Research
Cassandra Nunez
Undergraduate Student, Electrical Engineering

Cassandra Nunez joined the Ebrahimi group in fall 2019. She is currently working on studying bacterial pathogens using label-free, in situ diagnostic methods. In particular, she is working on studying change of cellular motion and morphological properties in response to environmental triggers, such as heat shock and antibiotics, and understanding how specific mutations affect their properties. The methodology is based on monitoring cellular motion using a simple-to-use optical scattering method. In the future, simultaneous monitoring of other phenotypes (metabolism/respiration) will also be enabled through the rational design of a multimodal sensory platform.
Virtual Reality Modeling of Cardiac Cells for Smart Health

Brain Byrne, Bernardo Crespo, and Zihang Qiu developed a virtual reality model of cardiac cells to illustrate how Sodium, Potassium, and Calcium ions flow through the cell membrane.
The objective of this undergraduate research project is to develop an immersive virtual reality (VR) system to enable researchers and students to actively practice, feel, and interact with cardiac electromechanical function. In the digital world, there will be ions (ca2+, Na+, K+ ... ), membranes, ion flows, ionic gates, cell contractions, action potentials, etc. Existing training courses in medicine and biology are cost prohibitive to go from cells to the whole heart in the classroom. Instructive courses lack the feeling of "presence" or "immersion" and are limited in the ability to interact with the cell models and digital environments in real time.

Upcoming Events
July 20-24, 2020
Montréal, Québec, Canada

The IEEE Engineering in Medicine and Biology Society is pleased to announce the 42nd Annual International Conferen ce of the IEEE Engineering in Medicine and Biology Society in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society, to be held in Montréal, Québec, Canada, July 20 - 24, 2020.  Please refer to the link:  for more information.

IISE Annual Conference 2020
May 30-June 2, 2020
New Orleans, Louisiana

The Institute of Industrial and Systems Engineers (IISE) is excited to invite you to New Orleans for an educational feast fit for the profession's finest. At the IISE Annual Conference & Expo, you're joining leaders in the field, up-and-comers, and students to network, gather new ideas, and learn about innovative tools and techniques. Prepare to make connections that will aid your career and build friendships that last a lifetime.  Please refer to the link:  for more information.

August 20-24, 2020, Hong Kong

The IEEE CASE is the flagship automation conference of the IEEE Robotics and Automation Society and constitutes the primary forum for cross-industry and multidisciplinary research in automation. Its goal is to provide a broad coverage and dissemination of fundamental research in automation among researchers, academics, and practitioners. The theme of the conference is  Automation AnalyticsPlease refer to the link:  for more information.
NEW CHOT website is launched
We have just redesigned and launched the new CHOT website. Please visit for more information about our new projects and exciting activities in the Penn State CHOT.